Overview

Dataset statistics

Number of variables10
Number of observations339396
Missing cells258572
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 MiB
Average record size in memory88.0 B

Variable types

Numeric10

Alerts

DO is highly overall correlated with TempHigh correlation
Temp is highly overall correlated with DOHigh correlation
pH has 48498 (14.3%) missing valuesMissing
EC has 68935 (20.3%) missing valuesMissing
DO has 47205 (13.9%) missing valuesMissing
ORP has 44640 (13.2%) missing valuesMissing
Temp has 49294 (14.5%) missing valuesMissing
EC has 5233 (1.5%) zerosZeros
TP has 5519 (1.6%) zerosZeros

Reproduction

Analysis started2023-03-04 14:41:17.119235
Analysis finished2023-03-04 14:41:52.187698
Duration35.07 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

pH
Real number (ℝ)

Distinct1164
Distinct (%)0.4%
Missing48498
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean7.3803322
Minimum0.11
Maximum13.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:52.601743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile5.06
Q16.32
median7.13
Q37.93
95-th percentile11.55
Maximum13.03
Range12.92
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.8726389
Coefficient of variation (CV)0.25373369
Kurtosis1.1175093
Mean7.3803322
Median Absolute Deviation (MAD)0.8
Skewness0.6796879
Sum2146923.9
Variance3.5067764
MonotonicityNot monotonic
2023-03-04T21:41:53.056886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.32 1561
 
0.5%
7.4 1554
 
0.5%
7.31 1527
 
0.4%
7.3 1486
 
0.4%
7.33 1461
 
0.4%
7.39 1460
 
0.4%
7.34 1444
 
0.4%
7.37 1439
 
0.4%
7.35 1415
 
0.4%
7.36 1409
 
0.4%
Other values (1154) 276142
81.4%
(Missing) 48498
 
14.3%
ValueCountFrequency (%)
0.11 3
 
< 0.1%
0.12 3
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.17 2
 
< 0.1%
0.18 5
 
< 0.1%
0.19 2
 
< 0.1%
0.21 9
 
< 0.1%
0.24 11
 
< 0.1%
0.25 29
< 0.1%
ValueCountFrequency (%)
13.03 1
 
< 0.1%
13.02 3
< 0.1%
13.01 4
< 0.1%
13 2
 
< 0.1%
12.99 2
 
< 0.1%
12.98 3
< 0.1%
12.97 1
 
< 0.1%
12.95 2
 
< 0.1%
12.94 4
< 0.1%
12.93 6
< 0.1%

EC
Real number (ℝ)

MISSING  ZEROS 

Distinct87913
Distinct (%)32.5%
Missing68935
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean27392.238
Minimum0
Maximum86525
Zeros5233
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:53.374493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.25
Q12732.29
median36970.83
Q344373.96
95-th percentile50000
Maximum86525
Range86525
Interquartile range (IQR)41641.67

Descriptive statistics

Standard deviation19748.639
Coefficient of variation (CV)0.7209575
Kurtosis-1.5620969
Mean27392.238
Median Absolute Deviation (MAD)12208.33
Skewness-0.40581513
Sum7.4085321 × 109
Variance3.9000876 × 108
MonotonicityNot monotonic
2023-03-04T21:41:53.658135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 32166
 
9.5%
9.38 6502
 
1.9%
6.25 5249
 
1.5%
0 5233
 
1.5%
15.62 4142
 
1.2%
1000 3682
 
1.1%
12.5 2808
 
0.8%
3.13 2525
 
0.7%
18.75 1317
 
0.4%
25 609
 
0.2%
Other values (87903) 206228
60.8%
(Missing) 68935
 
20.3%
ValueCountFrequency (%)
0 5233
1.5%
0.01 40
 
< 0.1%
0.03 1
 
< 0.1%
0.11 1
 
< 0.1%
0.23 1
 
< 0.1%
0.52 170
 
0.1%
0.53 4
 
< 0.1%
0.63 22
 
< 0.1%
0.78 54
 
< 0.1%
0.79 2
 
< 0.1%
ValueCountFrequency (%)
86525 1
< 0.1%
73776.96 1
< 0.1%
62009.03 1
< 0.1%
60652.27 1
< 0.1%
58177.7 1
< 0.1%
58068.24 1
< 0.1%
57098.71 1
< 0.1%
56310.07 1
< 0.1%
55292.7 1
< 0.1%
53669.17 1
< 0.1%

DO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct969
Distinct (%)0.3%
Missing47205
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean5.9551256
Minimum0
Maximum20
Zeros358
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:53.968934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.12
Q14.02
median6.81
Q37.58
95-th percentile8.32
Maximum20
Range20
Interquartile range (IQR)3.56

Descriptive statistics

Standard deviation2.2955295
Coefficient of variation (CV)0.38547121
Kurtosis4.4576368
Mean5.9551256
Median Absolute Deviation (MAD)1.19
Skewness0.24381701
Sum1740034.1
Variance5.2694556
MonotonicityNot monotonic
2023-03-04T21:41:54.331733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 3115
 
0.9%
20 1160
 
0.3%
7.12 1155
 
0.3%
7.11 1153
 
0.3%
7.21 1142
 
0.3%
7.22 1129
 
0.3%
7.19 1116
 
0.3%
7.1 1105
 
0.3%
7.2 1100
 
0.3%
7.14 1099
 
0.3%
Other values (959) 278917
82.2%
(Missing) 47205
 
13.9%
ValueCountFrequency (%)
0 358
 
0.1%
0.01 3115
0.9%
0.02 188
 
0.1%
0.03 65
 
< 0.1%
0.04 4
 
< 0.1%
0.05 2
 
< 0.1%
0.06 5
 
< 0.1%
0.07 5
 
< 0.1%
0.08 2
 
< 0.1%
0.09 5
 
< 0.1%
ValueCountFrequency (%)
20 1160
0.3%
15.14 1
 
< 0.1%
13.67 1
 
< 0.1%
13.54 1
 
< 0.1%
13.25 1
 
< 0.1%
12.89 1
 
< 0.1%
11.92 1
 
< 0.1%
11.02 2
 
< 0.1%
10.55 1
 
< 0.1%
10.38 1
 
< 0.1%

TSS
Real number (ℝ)

Distinct207464
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.31606
Minimum0
Maximum999
Zeros1037
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:54.676021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.49
Q113.81
median56.220732
Q3156.53116
95-th percentile335.19184
Maximum999
Range999
Interquartile range (IQR)142.72116

Descriptive statistics

Standard deviation135.16586
Coefficient of variation (CV)1.2834307
Kurtosis13.650699
Mean105.31606
Median Absolute Deviation (MAD)50.060732
Skewness2.9328835
Sum35743850
Variance18269.81
MonotonicityNot monotonic
2023-03-04T21:41:54.952394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.44 2654
 
0.8%
999 2534
 
0.7%
1.56 1959
 
0.6%
1.38 1823
 
0.5%
1.06 1626
 
0.5%
0 1037
 
0.3%
1.12 988
 
0.3%
1.5 931
 
0.3%
1.62 899
 
0.3%
1.25 783
 
0.2%
Other values (207454) 324162
95.5%
ValueCountFrequency (%)
0 1037
0.3%
0.006045783134 1
 
< 0.1%
0.01 33
 
< 0.1%
0.01154646053 1
 
< 0.1%
0.01435289532 1
 
< 0.1%
0.01593086682 1
 
< 0.1%
0.01782574825 1
 
< 0.1%
0.01926957067 1
 
< 0.1%
0.02 45
 
< 0.1%
0.02117326872 1
 
< 0.1%
ValueCountFrequency (%)
999 2534
0.7%
998.77 1
 
< 0.1%
956.75 1
 
< 0.1%
934.91 1
 
< 0.1%
885.63 1
 
< 0.1%
880.96 1
 
< 0.1%
878.8112226 1
 
< 0.1%
851.02 1
 
< 0.1%
804.37 1
 
< 0.1%
792.64 1
 
< 0.1%

TN
Real number (ℝ)

Distinct329680
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6036081
Minimum0
Maximum20
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:55.211365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.84885892
Q12.3539338
median3.5343625
Q34.7520126
95-th percentile6.5695177
Maximum20
Range20
Interquartile range (IQR)2.3980788

Descriptive statistics

Standard deviation1.7361548
Coefficient of variation (CV)0.48178236
Kurtosis0.79292415
Mean3.6036081
Median Absolute Deviation (MAD)1.1982957
Skewness0.38576339
Sum1223050.2
Variance3.0142336
MonotonicityNot monotonic
2023-03-04T21:41:55.452225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.51 186
 
0.1%
3.5 178
 
0.1%
3.56 149
 
< 0.1%
3.62 147
 
< 0.1%
3.46 141
 
< 0.1%
3.58 139
 
< 0.1%
3.44 136
 
< 0.1%
3.25 129
 
< 0.1%
3.57 123
 
< 0.1%
3.54 122
 
< 0.1%
Other values (329670) 337946
99.6%
ValueCountFrequency (%)
0 17
< 0.1%
0.0001943912385 1
 
< 0.1%
0.0004321799478 1
 
< 0.1%
0.000520341101 1
 
< 0.1%
0.0006229090311 1
 
< 0.1%
0.0007151136787 1
 
< 0.1%
0.0008995685182 1
 
< 0.1%
0.001294563841 1
 
< 0.1%
0.001373222761 1
 
< 0.1%
0.001631580413 1
 
< 0.1%
ValueCountFrequency (%)
20 41
< 0.1%
16.36 1
 
< 0.1%
14.89 12
 
< 0.1%
13.79 3
 
< 0.1%
13.67 1
 
< 0.1%
12.91 1
 
< 0.1%
12.65 1
 
< 0.1%
12.42 1
 
< 0.1%
11.77 1
 
< 0.1%
11.75168487 1
 
< 0.1%

TP
Real number (ℝ)

Distinct328979
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.536543
Minimum0
Maximum1381.17
Zeros5519
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:55.679245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5214867
Q127.253996
median61.186755
Q3105.80048
95-th percentile180.87158
Maximum1381.17
Range1381.17
Interquartile range (IQR)78.54648

Descriptive statistics

Standard deviation58.480304
Coefficient of variation (CV)0.80621852
Kurtosis26.712804
Mean72.536543
Median Absolute Deviation (MAD)37.88706
Skewness2.193677
Sum24618613
Variance3419.946
MonotonicityNot monotonic
2023-03-04T21:41:55.838225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5519
 
1.6%
0.02 809
 
0.2%
0.01 611
 
0.2%
0.03 463
 
0.1%
0.05 217
 
0.1%
0.04 199
 
0.1%
0.07 159
 
< 0.1%
0.06 105
 
< 0.1%
0.1 94
 
< 0.1%
0.11 87
 
< 0.1%
Other values (328969) 331133
97.6%
ValueCountFrequency (%)
0 5519
1.6%
0.0001119759281 1
 
< 0.1%
0.0002522438245 1
 
< 0.1%
0.000351450711 1
 
< 0.1%
0.0004413982976 1
 
< 0.1%
0.0005256530384 1
 
< 0.1%
0.0007113401034 1
 
< 0.1%
0.0008474424665 1
 
< 0.1%
0.001354511027 1
 
< 0.1%
0.001925187992 1
 
< 0.1%
ValueCountFrequency (%)
1381.17 1
< 0.1%
1375.17 1
< 0.1%
1369.17 1
< 0.1%
1363.17 1
< 0.1%
1357.17 1
< 0.1%
1351.17 1
< 0.1%
1345.17 1
< 0.1%
1339.17 1
< 0.1%
1333.17 1
< 0.1%
1327.17 1
< 0.1%

TOC
Real number (ℝ)

Distinct338371
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.813269
Minimum0
Maximum41
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:55.994216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5642286
Q111.649007
median18.55463
Q325.68679
95-th percentile35.048917
Maximum41
Range41
Interquartile range (IQR)14.037783

Descriptive statistics

Standard deviation9.4453611
Coefficient of variation (CV)0.50205847
Kurtosis-0.71566889
Mean18.813269
Median Absolute Deviation (MAD)7.0138092
Skewness0.12765069
Sum6385148.2
Variance89.214846
MonotonicityNot monotonic
2023-03-04T21:41:56.139119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 82
 
< 0.1%
0.89 15
 
< 0.1%
0.2 13
 
< 0.1%
3.86 13
 
< 0.1%
0.76 12
 
< 0.1%
0.52 12
 
< 0.1%
0.79 10
 
< 0.1%
0.84 9
 
< 0.1%
0.13 9
 
< 0.1%
0.86 8
 
< 0.1%
Other values (338361) 339213
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.0001146200161 1
< 0.1%
0.0005736775409 1
< 0.1%
0.0005897045089 1
< 0.1%
0.0006757858735 1
< 0.1%
0.0009095416959 1
< 0.1%
0.00130479049 1
< 0.1%
0.001459512784 1
< 0.1%
0.001736374588 1
< 0.1%
0.002509600606 1
< 0.1%
ValueCountFrequency (%)
41 2
< 0.1%
40.99944 1
< 0.1%
40.9960029 1
< 0.1%
40.99599588 1
< 0.1%
40.99469003 1
< 0.1%
40.9936535 1
< 0.1%
40.99312567 1
< 0.1%
40.99292974 1
< 0.1%
40.99265465 1
< 0.1%
40.99213859 1
< 0.1%

ORP
Real number (ℝ)

Distinct61284
Distinct (%)20.8%
Missing44640
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean347.30422
Minimum-1500.62
Maximum1000
Zeros1
Zeros (%)< 0.1%
Negative12621
Negative (%)3.7%
Memory size5.2 MiB
2023-03-04T21:41:56.288378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1500.62
5-th percentile49.195
Q1202.86
median339.56
Q3469.6125
95-th percentile876.0625
Maximum1000
Range2500.62
Interquartile range (IQR)266.7525

Descriptive statistics

Standard deviation315.13386
Coefficient of variation (CV)0.90737125
Kurtosis9.515165
Mean347.30422
Median Absolute Deviation (MAD)134.54
Skewness-1.5597055
Sum1.0237 × 108
Variance99309.352
MonotonicityNot monotonic
2023-03-04T21:41:56.442860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 7781
 
2.3%
-1500.6 673
 
0.2%
-1500.58 650
 
0.2%
-1500.62 497
 
0.1%
-1500.56 454
 
0.1%
-1500.54 218
 
0.1%
89.38 84
 
< 0.1%
381.5 78
 
< 0.1%
88.62 76
 
< 0.1%
81.88 70
 
< 0.1%
Other values (61274) 284175
83.7%
(Missing) 44640
 
13.2%
ValueCountFrequency (%)
-1500.62 497
0.1%
-1500.6 673
0.2%
-1500.59 1
 
< 0.1%
-1500.58 650
0.2%
-1500.57 14
 
< 0.1%
-1500.56 454
0.1%
-1500.55 6
 
< 0.1%
-1500.54 218
 
0.1%
-1500.53 1
 
< 0.1%
-1500.52 67
 
< 0.1%
ValueCountFrequency (%)
1000 7781
2.3%
999.98 8
 
< 0.1%
999.96 10
 
< 0.1%
999.94 10
 
< 0.1%
999.92 3
 
< 0.1%
999.91 1
 
< 0.1%
999.9 3
 
< 0.1%
999.88 2
 
< 0.1%
999.87 3
 
< 0.1%
999.85 5
 
< 0.1%

Temp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2252
Distinct (%)0.8%
Missing49294
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean27.540814
Minimum3.61
Maximum59.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:56.589371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile21.72
Q124.31
median27.1
Q330.83
95-th percentile33.55
Maximum59.39
Range55.78
Interquartile range (IQR)6.52

Descriptive statistics

Standard deviation3.9047639
Coefficient of variation (CV)0.14178099
Kurtosis-0.16871435
Mean27.540814
Median Absolute Deviation (MAD)3.21
Skewness0.29135135
Sum7989645.3
Variance15.247181
MonotonicityNot monotonic
2023-03-04T21:41:56.734497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.6 950
 
0.3%
25.9 933
 
0.3%
21.62 916
 
0.3%
25.44 873
 
0.3%
24.97 855
 
0.3%
22.1 848
 
0.2%
24.12 778
 
0.2%
23.56 755
 
0.2%
24.96 737
 
0.2%
23.54 723
 
0.2%
Other values (2242) 281734
83.0%
(Missing) 49294
 
14.5%
ValueCountFrequency (%)
3.61 1
< 0.1%
4.9 1
< 0.1%
5.43 1
< 0.1%
8.51 1
< 0.1%
8.79 1
< 0.1%
10.85 1
< 0.1%
12.39 1
< 0.1%
13.56 1
< 0.1%
14.86 1
< 0.1%
14.92 1
< 0.1%
ValueCountFrequency (%)
59.39 1
< 0.1%
59.28 1
< 0.1%
59.14 1
< 0.1%
59.13 1
< 0.1%
58.96 1
< 0.1%
58.87 1
< 0.1%
58.85 1
< 0.1%
58.8 1
< 0.1%
58.72 1
< 0.1%
58.68 1
< 0.1%

TEMP
Real number (ℝ)

Distinct338319
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.89239
Minimum21.18
Maximum39.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T21:41:56.892898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21.18
5-th percentile23.535954
Q127.112945
median29.852345
Q332.627631
95-th percentile36.382148
Maximum39.33
Range18.15
Interquartile range (IQR)5.5146861

Descriptive statistics

Standard deviation3.8413333
Coefficient of variation (CV)0.12850539
Kurtosis-0.55924557
Mean29.89239
Median Absolute Deviation (MAD)2.7583043
Skewness0.05588391
Sum10145357
Variance14.755842
MonotonicityNot monotonic
2023-03-04T21:41:57.047966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.26 13
 
< 0.1%
24.98 10
 
< 0.1%
29.74 9
 
< 0.1%
25 9
 
< 0.1%
35 9
 
< 0.1%
31.86 9
 
< 0.1%
35.88 9
 
< 0.1%
28.93 8
 
< 0.1%
35.24 8
 
< 0.1%
36.38 8
 
< 0.1%
Other values (338309) 339304
> 99.9%
ValueCountFrequency (%)
21.18 1
< 0.1%
21.1803509 1
< 0.1%
21.18074945 1
< 0.1%
21.18089344 1
< 0.1%
21.18111111 1
< 0.1%
21.18124418 1
< 0.1%
21.18134117 1
< 0.1%
21.182297 1
< 0.1%
21.18244275 1
< 0.1%
21.18266495 1
< 0.1%
ValueCountFrequency (%)
39.33 1
< 0.1%
39.32987303 1
< 0.1%
39.3294811 1
< 0.1%
39.32932315 1
< 0.1%
39.32863458 1
< 0.1%
39.32716928 1
< 0.1%
39.32649945 1
< 0.1%
39.32630934 1
< 0.1%
39.32611579 1
< 0.1%
39.32606748 1
< 0.1%

Interactions

2023-03-04T21:41:47.361415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:24.709058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:27.517300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:30.552495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:33.172522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:35.562083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:38.107414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:40.747550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:42.839200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:45.255724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:47.581068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:25.024203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:27.774963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:30.773722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:33.418306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:35.776777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:38.320574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:40.939077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:43.119352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:45.454610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:47.804083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:25.259088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:28.112068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:30.990658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:33.624471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:36.051094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:38.529518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:41.134374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:43.364276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:45.649233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:48.064581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:25.596384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:28.480171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:31.255744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:33.854750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:36.423067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:38.761872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:41.355093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:43.669997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:45.866591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:48.297583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:25.832682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:28.840673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:31.489460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:34.096221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:36.698507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:39.143156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:41.564304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:43.896870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:46.073186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:48.543265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:26.079176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:29.258194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:31.749483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:34.343730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:36.951866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:39.515578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:41.780738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:44.137660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:46.297882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:48.790080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:26.309742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:29.636973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:31.979376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:34.557963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:37.199316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:39.769373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:41.995206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:44.403541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:46.517596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:49.020663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:26.627453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:29.890370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:32.239372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:34.814194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:37.419158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:39.980361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:42.197605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:44.619290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:46.706356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:49.263011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:26.909478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:30.124913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:32.484303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:35.043952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:37.646567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:40.188149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:42.410394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:44.830809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:46.908059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:49.499366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:27.237448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:30.330580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:32.896390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:35.305394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:37.877633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:40.405672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:42.629510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:45.034811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T21:41:47.127638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-04T21:41:57.196630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
pHECDOTSSTNTPTOCORPTempTEMP
pH1.0000.0830.0940.059-0.004-0.022-0.003-0.3170.0710.002
EC0.0831.000-0.4400.1180.0060.0210.002-0.1110.235-0.000
DO0.094-0.4401.0000.076-0.0070.0260.003-0.155-0.512-0.000
TSS0.0590.1180.0761.0000.0020.031-0.0000.011-0.128-0.001
TN-0.0040.006-0.0070.0021.0000.006-0.002-0.0020.003-0.004
TP-0.0220.0210.0260.0310.0061.000-0.003-0.008-0.0140.002
TOC-0.0030.0020.003-0.000-0.002-0.0031.000-0.004-0.002-0.003
ORP-0.317-0.111-0.1550.011-0.002-0.008-0.0041.0000.1850.002
Temp0.0710.235-0.512-0.1280.003-0.014-0.0020.1851.0000.001
TEMP0.002-0.000-0.000-0.001-0.0040.002-0.0030.0020.0011.000

Missing values

2023-03-04T21:41:49.867469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-04T21:41:50.286328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-04T21:41:51.451981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pHECDOTSSTNTPTOCORPTempTEMP
Time
2017-07-11 14:05:007.291000.00.0137.722.382600118.30176620.640303257.6232.6428.204791
2017-07-11 14:10:007.291000.00.0137.187.28457123.18287614.900992260.5032.5622.112042
2017-07-11 14:15:007.291000.00.0136.644.66897211.36309921.685466255.5432.5433.116497
2017-07-11 14:20:007.301000.00.0136.252.14671083.47461324.299291255.0632.4727.300682
2017-07-11 14:25:007.311000.00.0136.082.93431212.72558736.730378258.6232.4233.866263
2017-07-11 14:30:007.311000.00.0135.835.59466379.63950911.190794260.1032.4238.535924
2017-07-11 14:35:007.311000.00.0135.631.89190092.43836928.065020258.9232.4224.901455
2017-07-11 14:40:007.311000.00.0135.523.54802626.13504225.175644255.4232.3526.988880
2017-07-11 14:45:007.311000.00.0135.353.075528130.6204278.040837252.5232.3029.320082
2017-07-11 14:50:007.311000.00.0135.262.058291235.7046568.779740252.1232.2729.274177
pHECDOTSSTNTPTOCORPTempTEMP
Time
2020-10-02 00:15:0012.789.388.3086.5189214.04939256.54903321.018809384.3524.6430.558691
2020-10-02 00:20:0012.799.388.2966.9798483.946797155.5400439.907424383.5824.6728.146237
2020-10-02 00:25:0012.829.388.3096.2795054.82614134.11842421.399300384.1724.6936.198463
2020-10-02 00:30:0012.799.388.29133.5375014.77513537.84334221.170736383.8824.7531.043348
2020-10-02 00:35:0012.829.388.2782.9989045.80193384.53223713.370508383.3124.7627.773501
2020-10-02 00:40:0012.789.388.2891.4466543.20827162.40356715.988773384.2124.7634.115773
2020-10-02 00:45:0012.809.388.27209.7492301.39828347.29734738.411562383.6324.7626.268491
2020-10-02 00:50:0012.819.388.27201.6560976.552750145.28827511.178243384.0424.7627.838110
2020-10-02 00:55:0012.789.388.27176.8806135.85791380.2600971.642902384.0824.7736.598129
2020-10-02 01:00:0012.809.388.28168.4043212.81432126.74428627.944556384.2324.7638.210078